Lesson Contents

• Introduction to Statistics
• What is Statistical Analysis?
• Importance of Statistics in Data Science and Research
• Types of Statistics
  • Descriptive Statistics
  • Inferential Statistics
• Population vs. Sample
• Types of Variables
  • Numerical Variables
  • Categorical Variables
  • Continuous Variables
  • Discrete Variables
• Levels of Measurement
  • Nominal
  • Ordinal
  • Interval
  • Ratio
• Data Collection Methods
• Sampling Techniques
  • Probability Sampling
  • Non-Probability Sampling
• Random Sampling
• Sampling Error and Bias
• Frequency Distributions
• Measures of Central Tendency
  • Mean
  • Median
  • Mode
• Measures of Dispersion
  • Range
  • Variance
  • Standard Deviation
  • Interquartile Range (IQR)
  • Coefficient of Variation
• Percentiles and Quartiles
• Skewness and Kurtosis
• Probability Concepts
• Common Probability Distributions
  • Normal Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Uniform Distribution
• The Central Limit Theorem
• Standard Error
• Confidence Intervals
• Statistical Estimation
• Introduction to Hypothesis Testing
• Null and Alternative Hypotheses
• Type I and Type II Errors
• Significance Level (α)
• P-values and Their Interpretation
• Statistical Assumptions
• Parametric vs. Non-Parametric Methods
• Choosing the Appropriate Statistical Test
• Descriptive Statistics in R
• Visualizing Statistical Distributions
• Best Practices in Statistical Analysis
• Common Mistakes in Statistical Interpretation
• Practical Examples Using Real Datasets
• Hands-on Exercises
• Chapter Summary
• Practice Questions
• Assignment